def test_sample_weight_length():
# check that an error is raised when passing sample weights
# with an incompatible shape
km = KMeans(n_clusters=n_clusters, random_state=42)
msg = r'sample_weight.shape == \(2,\), expected \(100,\)'
with pytest.raises(ValueError, match=msg):
km.fit(X, sample_weight=np.ones(2))
def test_predict_equal_labels(algo):
km = KMeans(random_state=13,
n_jobs=1,
n_init=1,
max_iter=1,
algorithm=algo)
km.fit(X)
assert_array_equal(km.predict(X), km.labels_)
def test_k_means_copyx():
# Check if copy_x=False returns nearly equal X after de-centering.
my_X = X.copy()
km = KMeans(copy_x=False, n_clusters=n_clusters, random_state=42)
km.fit(my_X)
_check_fitted_model(km)
# check if my_X is centered
assert_array_almost_equal(my_X, X)
def test_supervised_cluster_scorers():
# Test clustering scorers against gold standard labeling.
X, y = make_blobs(random_state=0, centers=2)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
km = KMeans(n_clusters=3)
km.fit(X_train)
for name in CLUSTER_SCORERS:
score1 = get_scorer(name)(km, X_test, y_test)
score2 = getattr(cluster_module, name)(y_test, km.predict(X_test))
assert_almost_equal(score1, score2)
def test_transform():
km = KMeans(n_clusters=n_clusters)
km.fit(X)
X_new = km.transform(km.cluster_centers_)
for c in range(n_clusters):
assert X_new[c, c] == 0
for c2 in range(n_clusters):
if c != c2:
assert X_new[c, c2] > 0
def test_k_means_empty_cluster_relocated():
# check that empty clusters are correctly relocated when using sample
# weights (#13486)
X = np.array([[-1], [1]])
sample_weight = [1.9, 0.1]
init = np.array([[-1], [10]])
km = KMeans(n_clusters=2, init=init, n_init=1)
km.fit(X, sample_weight=sample_weight)
assert len(set(km.labels_)) == 2
assert_allclose(km.cluster_centers_, [[-1], [1]])
def test_k_means_init_centers():
# This test is used to check KMeans won't mutate the user provided input
# array silently even if input data and init centers have the same type
X_small = np.array([[1.1, 1.1], [-7.5, -7.5], [-1.1, -1.1], [7.5, 7.5]])
init_centers = np.array([[0.0, 0.0], [5.0, 5.0], [-5.0, -5.0]])
for dtype in [np.int32, np.int64, np.float32, np.float64]:
X_test = dtype(X_small)
init_centers_test = dtype(init_centers)
assert_array_equal(init_centers, init_centers_test)
km = KMeans(init=init_centers_test, n_clusters=3, n_init=1)
km.fit(X_test)
assert np.may_share_memory(km.cluster_centers_, init_centers) is False
def test_k_means_fortran_aligned_data():
# Check the KMeans will work well, even if X is a fortran-aligned data.
X = np.asfortranarray([[0, 0], [0, 1], [0, 1]])
centers = np.array([[0, 0], [0, 1]])
labels = np.array([0, 1, 1])
km = KMeans(n_init=1,
init=centers,
precompute_distances=False,
random_state=42,
n_clusters=2)
km.fit(X)
assert_array_almost_equal(km.cluster_centers_, centers)
assert_array_equal(km.labels_, labels)
def test_score(algo):
# Check that fitting k-means with multiple inits gives better score
km1 = KMeans(n_clusters=n_clusters,
max_iter=1,
random_state=42,
n_init=1,
algorithm=algo)
s1 = km1.fit(X).score(X)
km2 = KMeans(n_clusters=n_clusters,
max_iter=10,
random_state=42,
n_init=1,
algorithm=algo)
s2 = km2.fit(X).score(X)
assert s2 > s1
def test_pipeline_spectral_clustering(seed=36):
# Test using pipeline to do spectral clustering
random_state = np.random.RandomState(seed)
se_rbf = SpectralEmbedding(n_components=n_clusters,
affinity="rbf",
random_state=random_state)
se_knn = SpectralEmbedding(n_components=n_clusters,
affinity="nearest_neighbors",
n_neighbors=5,
random_state=random_state)
for se in [se_rbf, se_knn]:
km = KMeans(n_clusters=n_clusters, random_state=random_state)
km.fit(se.fit_transform(S))
assert_array_almost_equal(
normalized_mutual_info_score(km.labels_, true_labels), 1.0, 2)
def test_k_means_fit_predict(algo, dtype, constructor, seed, max_iter, tol):
# check that fit.predict gives same result as fit_predict
# There's a very small chance of failure with elkan on unstructured dataset
# because predict method uses fast euclidean distances computation which
# may cause small numerical instabilities.
# NB: This test is largely redundant with respect to test_predict and
# test_predict_equal_labels. This test has the added effect of
# testing idempotence of the fittng procesdure which appears to
# be where it fails on some MacOS setups.
if sys.platform == "darwin":
pytest.xfail(
"Known failures on MacOS, See "
"https://github.com/scikit-learn/scikit-learn/issues/12644")
if not (algo == 'elkan' and constructor is sp.csr_matrix):
rng = np.random.RandomState(seed)
X = make_blobs(n_samples=1000,
n_features=10,
centers=10,
random_state=rng)[0].astype(dtype, copy=False)
X = constructor(X)
kmeans = KMeans(algorithm=algo,
n_clusters=10,
random_state=seed,
tol=tol,
max_iter=max_iter,
n_jobs=1)
labels_1 = kmeans.fit(X).predict(X)
labels_2 = kmeans.fit_predict(X)
assert_array_equal(labels_1, labels_2)
def test_sparse_validate_centers():
from mrex.datasets import load_iris
iris = load_iris()
X = iris.data
# Get a local optimum
centers = KMeans(n_clusters=4).fit(X).cluster_centers_
# Test that a ValueError is raised for validate_center_shape
classifier = KMeans(n_clusters=3, init=centers, n_init=1)
msg = r"The shape of the initial centers \(\(4L?, 4L?\)\) " \
"does not match the number of clusters 3"
with pytest.raises(ValueError, match=msg):
classifier.fit(X)
def test_k_means_non_collapsed():
# Check k_means with a bad initialization does not yield a singleton
# Starting with bad centers that are quickly ignored should not
# result in a repositioning of the centers to the center of mass that
# would lead to collapsed centers which in turns make the clustering
# dependent of the numerical unstabilities.
my_X = np.array([[1.1, 1.1], [0.9, 1.1], [1.1, 0.9], [0.9, 1.1]])
array_init = np.array([[1.0, 1.0], [5.0, 5.0], [-5.0, -5.0]])
km = KMeans(init=array_init, n_clusters=3, random_state=42, n_init=1)
km.fit(my_X)
# centers must not been collapsed
assert len(np.unique(km.labels_)) == 3
centers = km.cluster_centers_
assert np.linalg.norm(centers[0] - centers[1]) >= 0.1
assert np.linalg.norm(centers[0] - centers[2]) >= 0.1
assert np.linalg.norm(centers[1] - centers[2]) >= 0.1
def test_k_means_new_centers():
# Explore the part of the code where a new center is reassigned
X = np.array([[0, 0, 1, 1], [0, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0], [0, 1, 0, 0]])
labels = [0, 1, 2, 1, 1, 2]
bad_centers = np.array([[+0, 1, 0, 0], [.2, 0, .2, .2], [+0, 0, 0, 0]])
km = KMeans(n_clusters=3,
init=bad_centers,
n_init=1,
max_iter=10,
random_state=1)
for this_X in (X, sp.coo_matrix(X)):
km.fit(this_X)
this_labels = km.labels_
# Reorder the labels so that the first instance is in cluster 0,
# the second in cluster 1, ...
this_labels = np.unique(this_labels, return_index=True)[1][this_labels]
np.testing.assert_array_equal(this_labels, labels)
def test_elkan_results(distribution):
# check that results are identical between lloyd and elkan algorithms
rnd = np.random.RandomState(0)
if distribution == 'normal':
X = rnd.normal(size=(50, 10))
else:
X, _ = make_blobs(random_state=rnd)
km_full = KMeans(algorithm='full', n_clusters=5, random_state=0, n_init=1)
km_elkan = KMeans(algorithm='elkan',
n_clusters=5,
random_state=0,
n_init=1)
km_full.fit(X)
km_elkan.fit(X)
assert_array_almost_equal(km_elkan.cluster_centers_,
km_full.cluster_centers_)
assert_array_equal(km_elkan.labels_, km_full.labels_)
def test_kmeans_results(representation, algo, dtype):
# cheks that kmeans works as intended
array_constr = {'dense': np.array, 'sparse': sp.csr_matrix}[representation]
X = array_constr([[0, 0], [0.5, 0], [0.5, 1], [1, 1]], dtype=dtype)
sample_weight = [3, 1, 1, 3] # will be rescaled to [1.5, 0.5, 0.5, 1.5]
init_centers = np.array([[0, 0], [1, 1]], dtype=dtype)
expected_labels = [0, 0, 1, 1]
expected_inertia = 0.1875
expected_centers = np.array([[0.125, 0], [0.875, 1]], dtype=dtype)
expected_n_iter = 2
kmeans = KMeans(n_clusters=2, n_init=1, init=init_centers, algorithm=algo)
kmeans.fit(X, sample_weight=sample_weight)
assert_array_equal(kmeans.labels_, expected_labels)
assert_almost_equal(kmeans.inertia_, expected_inertia)
assert_array_almost_equal(kmeans.cluster_centers_, expected_centers)
assert kmeans.n_iter_ == expected_n_iter
data=data)
# in this case the seeding of the centers is deterministic, hence we run the
# kmeans algorithm only once with n_init=1
pca = PCA(n_components=n_digits).fit(data)
bench_k_means(KMeans(init=pca.components_, n_clusters=n_digits, n_init=1),
name="PCA-based",
data=data)
print(82 * '_')
# #############################################################################
# Visualize the results on PCA-reduced data
reduced_data = PCA(n_components=2).fit_transform(data)
kmeans = KMeans(init='k-means++', n_clusters=n_digits, n_init=10)
kmeans.fit(reduced_data)
# Step size of the mesh. Decrease to increase the quality of the VQ.
h = .02 # point in the mesh [x_min, x_max]x[y_min, y_max].
# Plot the decision boundary. For that, we will assign a color to each
x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1
y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# Obtain labels for each point in mesh. Use last trained model.
Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])
# Put the result into a color plot
Z = Z.reshape(xx.shape)
plt.figure(1)
# #############################################################################
# Generate sample data
np.random.seed(0)
batch_size = 45
centers = [[1, 1], [-1, -1], [1, -1]]
n_clusters = len(centers)
X, labels_true = make_blobs(n_samples=3000, centers=centers, cluster_std=0.7)
# #############################################################################
# Compute clustering with Means
k_means = KMeans(init='k-means++', n_clusters=3, n_init=10)
t0 = time.time()
k_means.fit(X)
t_batch = time.time() - t0
# #############################################################################
# Compute clustering with MiniBatchKMeans
mbk = MiniBatchKMeans(init='k-means++',
n_clusters=3,
batch_size=batch_size,
n_init=10,
max_no_improvement=10,
verbose=0)
t0 = time.time()
mbk.fit(X)
t_mini_batch = time.time() - t0
def test_iter_attribute():
# Regression test on bad n_iter_ value. Previous bug n_iter_ was one off
# it's right value (#11340).
estimator = KMeans(algorithm="elkan", max_iter=1)
estimator.fit(np.random.rand(10, 10))
assert estimator.n_iter_ == 1
def test_k_means_precompute_distances_flag():
# check that a warning is raised if the precompute_distances flag is not
# supported
km = KMeans(precompute_distances="wrong")
with pytest.raises(ValueError):
km.fit(X)
def test_k_means_init(data, init):
km = KMeans(init=init, n_clusters=n_clusters, random_state=42, n_init=1)
km.fit(data)
_check_fitted_model(km)